There have been proposed various methods for classifying (also called grouping or clustering) so-called discrete data into groups. The discrete data includes, for example, a point of sale system (POS) record with an identifier (ID), World Wide Web (WEB) access log record, and the like.
A discrete data analyst analyzes classified discrete data (in other words, a record of each group) for the purpose of inferring intentions and behaviors of people. For example, such an analyst analyzes the classified discrete data for the purpose of inferring purchase behavior based on common consumer demands and of inferring WEB browsing behavior based on a common interest.
As one of the methods for classifying the discrete data, there is a method of classifying discrete data by referring to a group evaluation value calculated based on an occurrence probability (also called appearance probability) of a record within a group and a constant multiple of the number of groups. A related technology is disclosed in “COOLCAT: an entropy-based algorithm for categorical clustering” by Daniel Barbara et al., CIKM (2002), for example.